Curriculum

The course surveys the ideas and concepts relating to consumers, producers and their interaction, it will provide technical means to solve practical problems and will also follow the development of these concepts over the years.

The first part of the course will introduce the tools and techniques for solving dynamic economic models, in particular deterministic and stochastic discrete-time dynamic programming. We will apply the tools to study the neoclassical growth model, consumption and saving choices, and labor search. Basic knowledge of Matlab will be required to solve some of the problem sets.

This is a graduate level introductory course in mathematical probability and statistics: its objective is to provide students with key conceptual tools that, in addition to being foundational to actual statistical analysis, are necessary for additional training in econometrics and microeconomics. Beginning from basic axiomatic definitions of probability, the course introduces univariate and multivariate probability distributions, samples and statistics, concepts of estimation (method of moments, maximum likelihood) and inference, some key asymptotic results, and it concludes with an introduction to linear projections and regression. Emphasis is placed throughout on how statistical concepts, methods and mathematical notation are conventionally adopted by economists and econometricians. For this reason, there is little to no coverage of Bayesian statistics and machine learning. Course assignments and examinations are based around typical technical exercises which often require the use of calculus and, in more limited occasions, linear algebra and computer coding. While minimal training in computer training is given in one of the exercise sessions, calculus and linear algebra are prerequisites.

This course is the second in a three-semester microeconomics sequence for first-year Ph.D. students, focusing on non-cooperative game theory and its applications. Game theory is a widely used mathematical tool for modeling and analyzing interactive decision-making situations. The course aims to introduce students to normal-form (static) and extensive-form (dynamic) games with complete and incomplete information. It will also cover some basic economic applications of game theory, preparing students for more advanced studies.

Ctirad Slavík: This is the first part of the second part of the Macroeconomics PhD sequence. It will be both about learning technical tools and about applying them. We will set up a pretty general CE model with infinite horizon, heterogenous firms and consumers. We will prove the First Welfare Theorem and discuss the Second Welfare Theorem for this environment. Then we will show how to simplify the model to the deterministic one sector growth model (aggregation). In the next part of the course, we will extend the model to account for the 2 most important features of current economies: long run growth (briefly as already done in Macro 1) and business cycle fluctuations. In the next part of the course, we will be interested in the government's role in the economy and the optimal fiscal policies. We will set up the classic Ramsey linear taxation problem and derive the celebrated Chamley-Judd result, which states that optimal taxes on capital are zero in the long run. Finally, we will discuss recent developments in the literature.
Michal Kejak: The second half of the course will focus on general equilibrium models of endogenous economic growth in continuous time. It will begin with the basics of dynamic optimization in continuous time and an outline of the stylized facts of economic growth. After applying dynamic optimization in continuous time to the Ramsey neoclassical growth model, including government and taxes, this part of the course will cover the first generation of endogenous growth models and models with endogenous technological change. Additionally, the simple overlapping generations model and the finite-lifetime model will be discussed.

The course presents technical aspects of modern econometric estimation and inference, applied in both cross-sectional and time-series settings. After reviewing important econometric notions and asymptotic inference tools, we concentrate on parametric regression models, including linear and nonlinear ones. Then we turn to methods applied to non-regression settings, including maximum likelihood and method of moments estimation. Finally, we will study methods of bootstrap inference.

This course focuses on PhD/professional level writing in Economics. We consider ways that writing at this level differs from other types of writing and students will practice their analytical writing skills in formal, post-graduate level English. There is an emphasis on accurate and effective citation and referencing, and the types of language used in professional texts in the field. The course includes lectures, peer review during the writing process, and individual consultations with the instructor. Extensive written feedback on the work is given with a view to supporting future writing.

The final task is a written analytical comparison of two related articles in the field of Economics. Students choose articles/topics that reflect their personal interests in the field.

The skills practiced on this course are designed to support student writing throughout their first two years of study and beyond.

This is the third and final course in the core micro sequence. It focuses on information economics. We start by considering what information is, and how rational agents learn from information. Then we consider problems of hidden information and adverse selection, and how they can be solved: Through communication (signaling, cheap talk, verifiable information and communication with full commitment) and screening. Problems of hidden action can lead to moral hazard, and we consider principal agent problems as well as the theory of contracts. The second half of the course focuses on mechanism design in general and by considering specific mechanisms (auctions and matching algorithms).

We will study a few papers that represent the current issues in applied macroeconomics such as how to explain the recent evolution of macroeconomic indicators, how to manage inflation, and how the US monetary policy affects the rest of the world. The emphasis in on how macro-economists address applied and policy-relevant issues rather than introducing new macroeconomic theory.

This course is the third part of the Econometrics sequence. The first two weeks, the course will cover non-parametric mean regression. From the third week on, we will build on the techniques from Econometrics I and start with binary choice models for cross sectional data. Next, we will discuss methods applied to panel data. Last, we will delve into causal inference, based on the methods previously learned and new methods introduced.

The goal of the course is to introduce tools necessary to understand and implement empirical studies (evaluations of causal effects) with cross-sectional and panel data. Heterogeneous treatment effects and dynamic panel data models fall outside of the scope of the course, as do machine learning techniques and AI. Examples from applied work will be used to illustrate the discussed methods. Note that the course covers much of the work of the Nobel prize laureates for 2000 and 2021. The main reference textbook for the course is Econometric Analysis of Cross Section and Panel Data, Jeffrey M. Wooldridge, MIT Press 2002. I provide suggestions for reading and additional references throughout the lecture notes (available on my homepage).

This is a graduate-level course on selected approaches in so-called “structural” econometric estimation, with emphasis on methods originally devised in industrial organization, but applicable also in different fields. Following a review of some key econometric concepts and tools (identification, estimation frameworks, discrete choice models), the course overviews the main econometric approaches adopted in selected areas of industrial organization, such as the estimation of demand and production functions, the analysis of strategic interactions (especially in the setting of oligopolistic competition), and spillover effects (with particular regard to cross-firm spillovers). An objective of the course is to endow attendants with some minimal computational tools that would enable them to implement the reviewed methods on actual data about markets and firms. A number of lectures, as well as many of the course assignments that inform the final grade, are built around coding exercises. While not strictly required, some degree of familiarity with a high-level programming language the likes of R, Python or Julia is desirable, as it would facilitate navigating the course.

The course will provide fundamental understanding of stylized labor supply and demand in their static and advanced versions, and associated models of wage determination. The course will combine theoretical concepts, empirical evidence and empirical methods including use of econometrics and individual level data. Policy and mechanism designs debates involving students will be encouraged.
The course has three major goals (i) to guide students through current theoretical and empirical understanding of major labor market issues, (ii) to promote student’s own empirical research on topic selected, (iii) to make students familiar with stylized research resources, field standards and approaches. Throughout the topics, empirical methodological approaches will be clarified (data and econometric / identification techniques).
The prerequisite for are principles of microeconomic theory, statistics and econometrics from the 1st year.

We will study several papers that represent the current state of macroeconomic research. I have chosen a few papers. The papers that I chose cover the topics of financial flows across country, the monetary policy transmission across countries, and the relation between the fiscal policy and the foreign debt management. We will choose additional papers to study in class.

This course is the first half of the sequence Dynamic Modeling in Economics, and covers important aspects of modern econometrics applied to time series data, both from macroeconomics and finance. After reviewing of basic time series notions, we will discuss principles of non-structural time series modeling and model selection procedures. Then, we will study models for conditional mean dynamics such as linear and nonlinear autoregressions, which are primarily applied to macroeconomic data. We will also study methods of dealing with structural instability, and will get acquainted with the notion of Brownian motion useful in other contexts as well. Then we will turn to modeling conditional variance and, more generally, volatility, as well as other conditional objects such as conditional quantiles, probabilities, densities, modeling which is primarily related to financial data.

We will revisit a few fundamental concepts of economic theory and try to improve their understanding. For instance, we will study basic implications of market via welfare theorems, and explore the limits to what markets can achieve and what it means for policy. The course will also help students to better understand fundamental results in political economy or behavioral economics.

This is a second-year graduate-level course. The course is based on selected and (mostly) recent empirical research papers focusing on particular aspects of the economic history of the United States, paying particular attention to the topics of internal and international migration, cities, innovation, and culture. Beyond providing students with an in-depth understanding of the research frontier in US economic history, the course will focus on developing skills in developing, communicating, presenting, and evaluating research ideas and causal research designs in applied economics more broadly.

This course is the second step in student’s ongoing practice of their professional communications skills in the broad field of economics. It includes written tasks, a negotiation, and presentations, and continues the collaborative features of Research Writing 1. Lectures, discussions, teamwork, and individual consultations with the instructor are aimed to continue to build student’s skills and confidence, and to provide useful take-aways for real-world endeavors. The skills practiced on this course are designed to support student writing and speaking throughout their studies and beyond into real-world contexts. The RW2 course includes a focus on MAER students’ early development of their required Master’s thesis. Development of the thesis will be supported via in-class work and individual consultation with the instructor.

The course aims at getting the second year Ph.D. students familiar with the basics and subtleties of how the academic economic science works and how academic economists do research, publish academic papers, make academic presentations and find their jobs. We will also review resources available on the Internet and aspects of academic integrity.

The main topics of the class are econometric approaches to the problem of sample selection and (individual-level) heterogeneity. While the methods apply more generally, the class will focus on methods to address the selection problem from the program evaluation literature and place particular emphasis on heterogeneity in randomized control trials in the second part of the course

This course focuses on the theoretical study of market power, covering key concepts and models in static and dynamic oligopoly theory, along with their applications. It examines how firms behave in industries where a few competitors interact strategically, meaning they must consider each other's actions. These strategic interactions have both positive (e.g., pricing, market structure, innovation intensity) and normative implications (e.g., competition policy). While the emphasis is on positive analysis, the course also frequently addresses the normative aspects.

This course focuses on economic development with an emphasis on economic history, culture, and political economy. We will use a historical and comparative approach to explore how societies evolve and develop. Specifically, we will examine research on whether differences in contemporary economic development have historical origins. We will also study the mechanisms and channels through which history influences development, with particular attention to the role of domestic institutions and culture in explaining historical persistence. While this research area uses the methods of economics, the research questions overlap with those in other disciplines like history, psychology, political science, anthropology, and geography. We will discuss methods for observational data as well as for survey data collection and field experiments.

Michal Kejak: The increasing complexity in the analysis of theoretical and applied dynamic macroeconomic models—primarily due to the lack of available analytic solutions for most of them, and when they do exist, they are often trivial simplifications of the original problem—necessitates the use of efficient numerical methods in macroeconomics. The first part of the course is devoted to elementary concepts of numerical analysis and basic numerical methods, while the second part focuses on numerical methods for solving dynamic macroeconomic models. Students will be expected to write their own simple programs and run application programs and toolboxes in MATLAB.
Ctirad Slavík: This is the second part of the Computational Macroeconomics course. The first part will be taught by Michal Kejak. In my part, we will discuss issues related to heterogeneity and inequality. We will start by characterizing the solution to a heterogeneous agents model with complete markets and conclude that the dynamics implied by the model are not consistent with real-world data. We will then analyze models in which markets are (exogeneously) incomplete - either because not all assets are traded (there is only one risk-free asset) or there are borrowing constraints. First, we will characterize the solution to individual agents' problems when the interest rate is fixed (partial equilibrium) with, first, deterministic and, second, stochastic, income fluctuations. Next, we will study general equilibrium versions of these models without and with aggregate risk.

The course will introduce several basic approaches to bounded rationality in macroeconomics, and discuss its implications for consumer behavior, optimal policies, and macroeconomic dynamics. We will cover adaptive learning, restricted perceptions equilibria, model switching, sparse rationality, imperfect information, cognitive discounting, and other ways of modeling bounded rationality; survey experimental evidence on learning and bounded rationality, and use a DYNARE toolbox for estimation of DSGE models under adaptive learning. We will cover recent advances in DSGE models’ estimation, such as Hamiltonian Monte Carlo, Active Subspace Monte Carlo, and Machine Learning approaches. We will also survey a recent literature on formation of expectations, especially of inflation expectations, and on consistency of survey expectations with Full Information Rational Expectations (FIRE) assumption. The grade for this Part is based on homeworks (20%), exam (40%), and a project (30%). An additional 10% will be allocated based on in-class presentations of assigned papers.

This course, intended for second-year Ph.D. students interested in Micro Theory and Game Theory, introduces a selection of topics and research frontiers in these fields. Topics covered include modeling incomplete information (Aumann model and Universal belief space), the Bayesian framework and information structure (Blackwell experiments), observational learning, experimentation (exploitation vs. exploration), repeated games and reputation, among other emerging topics. The course aims to prepare students for conducting their own research.

The course will bring students to the research frontier in applied economics with a special emphasis on economic history and long-run development. The course consists of weekly lectures and seminars in which we discuss topics such as pre-industrial development, industrialization, the formation of norms for long-run economic outcomes, war economics, the economics of crises, the economics of totalitarian regimes, regional development after World War II and more recent figures of economic growth, transition, and monetary integration. The lectures will provide stylized facts and underlying theoretical concepts, while we will critically discuss recent empirical research papers on the respective topics during the seminars. The course further consists of Stata assignments in which students will challenge published papers with newly established methodological. In the end, students have to prepare and present their own research proposal in the field of quantitative economic history.

The course focuses on modern monetary economics, both from a theoretical and practical policy-making perspective. It covers the role of money in the economy, endogenous money creation in the modern monetary system, monetary policy instruments in normal times as well as the unconventional monetary policy tools. The transmission mechanism of monetary policy is analysed both in partial and general equilibrium. The optimal institutional design for modern central banks is discussed within the dynamic inconsistency model for monetary policy, including the discussion of its political-economy background, consequences and potential solutions. The inflation targeting framework is presented both for closed and small-open economies, and contrasted to alternative policy frameworks, including pegged exchange rate arrangements and price-level targeting. Finally, the nexus between monetary policy and other policy areas is explored, including the interaction with fiscal and macro-prudential policies.

This graduate-level course introduces machine learning techniques and applications tailored for the social sciences. It aims to equip students with essential tools to apply machine learning in different areas, including causal inference and time-series analysis. The course combines practical Python applications with foundational statistical methods. Topics include generalized linear models, decision trees, and neural networks, providing a solid foundation in core machine learning approaches. By the end of the course, students will have a comprehensive understanding of key machine learning paradigms.

This is the final required credit course for the Academic Skills Center. The seminar is designed primarily to assist dissertation proposal workshop participants with their written research proposals and presentations via consultation with Academic Skills Center faculty. For DPW candidates, the seminar will work towards the first official DPW draft due November 1st (or when the SAO announces). Consultations will continue through DPW week. All students deliver a practice presentation of their research proposals prior to DPW week. Students not wishing to participate in DPW can complete the course requirements by participating in all elements of the course without final attendance at DPW.

The course aims at getting the second year Ph.D. students familiar with the basics and subtleties of how the academic economic science works and how academic economists do research, publish academic papers, make academic presentations and find their jobs. We will also review resources available on the Internet and aspects of academic integrity.

CS2 MA is a consultation-based course with one introductory lecture designed to support students writing a master’s paper in fulfillment of the US MA degree requirement at CERGE-EI. Enrollment in CS2 MA is a requirement for submitting an MA paper. The SAO will notify you if you are enrolled in CS2MA and will inform you of when the introductory lecture will be held.
Students should consult with both the ASC instructor and the Economics instructor on their plans for the paper in terms of type and proposed content early in the semester.
The papers are graded on the CS2MA course as per normal ASC writing standards. They must pass on the Economics side for content in order for an MA degree to be awarded.